In [15]:
df.groupby(['Temperature_F','Humidity_%'])['PM2.5_ATM_ug/m3'].mean().plot()
Out[15]:
<AxesSubplot:xlabel='Temperature_F,Humidity_%'>
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.notebook import tqdm
from sklearn.preprocessing import MinMaxScaler,StandardScaler
import warnings
import random
warnings.filterwarnings('ignore')
%matplotlib inline
import torch
from torch import nn,optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import Dataset,DataLoader
device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
seed=42
random.seed(42)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
df=pd.read_csv('Port Harcourt, Nigeria (4.80678 7.002953).csv')
df.head(5)
| created_at | entry_id | Temperature_F | Humidity_% | PM2.5_ATM_ug/m3 | |
|---|---|---|---|---|---|
| 0 | 2021-07-26 16:45:59 UTC | 198 | 89 | 50 | 40.78 |
| 1 | 2021-07-26 16:48:03 UTC | 199 | 89 | 50 | 40.09 |
| 2 | 2021-07-26 16:49:59 UTC | 200 | 89 | 50 | 37.93 |
| 3 | 2021-07-26 16:54:03 UTC | 201 | 89 | 50 | 33.18 |
| 4 | 2021-07-26 16:55:59 UTC | 202 | 89 | 50 | 37.84 |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 56052 entries, 0 to 56051 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 created_at 56052 non-null object 1 entry_id 56052 non-null int64 2 Temperature_F 56052 non-null int64 3 Humidity_% 56052 non-null int64 4 PM2.5_ATM_ug/m3 56052 non-null float64 dtypes: float64(1), int64(3), object(1) memory usage: 2.1+ MB
df.isnull().sum()
created_at 0 entry_id 0 Temperature_F 0 Humidity_% 0 PM2.5_ATM_ug/m3 0 dtype: int64
df.drop(columns='entry_id',inplace=True)
df['created_at']=pd.to_datetime(df['created_at'])
df.set_index('created_at',inplace=True)
df.sort_index(inplace=True)
# TimeStamp
df.index.unique()
DatetimeIndex(['2021-07-26 16:45:59+00:00', '2021-07-26 16:48:03+00:00',
'2021-07-26 16:49:59+00:00', '2021-07-26 16:54:03+00:00',
'2021-07-26 16:55:59+00:00', '2021-07-26 16:58:11+00:00',
'2021-07-26 17:00:08+00:00', '2021-07-26 17:02:05+00:00',
'2021-07-26 17:04:02+00:00', '2021-07-26 17:06:00+00:00',
...
'2021-10-25 09:18:40+00:00', '2021-10-25 09:20:40+00:00',
'2021-10-25 09:22:42+00:00', '2021-10-25 09:24:44+00:00',
'2021-10-25 09:26:47+00:00', '2021-10-25 09:28:43+00:00',
'2021-10-25 09:30:43+00:00', '2021-10-25 09:32:44+00:00',
'2021-10-25 09:34:44+00:00', '2021-10-25 09:36:44+00:00'],
dtype='datetime64[ns, UTC]', name='created_at', length=56052, freq=None)
df.head(5)
| Temperature_F | Humidity_% | PM2.5_ATM_ug/m3 | |
|---|---|---|---|
| created_at | |||
| 2021-07-26 16:45:59+00:00 | 89 | 50 | 40.78 |
| 2021-07-26 16:48:03+00:00 | 89 | 50 | 40.09 |
| 2021-07-26 16:49:59+00:00 | 89 | 50 | 37.93 |
| 2021-07-26 16:54:03+00:00 | 89 | 50 | 33.18 |
| 2021-07-26 16:55:59+00:00 | 89 | 50 | 37.84 |
df.groupby(['Temperature_F','Humidity_%'])['PM2.5_ATM_ug/m3'].mean().plot()
<AxesSubplot:xlabel='Temperature_F,Humidity_%'>
It indicates the previous value relationship with current value at different time Interval
df_acf=[]
for lag in range(0,31):
df_acf.append(df['PM2.5_ATM_ug/m3'].autocorr(lag))
plt.plot(np.arange(0,31),df_acf)
[<matplotlib.lines.Line2D at 0x19893c9d5b0>]
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
plt.figure(figsize=(5.5, 5.5))
plot_pacf(df['PM2.5_ATM_ug/m3'], lags=20)
<Figure size 396x396 with 0 Axes>
plt.figure(figsize=(5.5, 5.5))
plot_acf(df['PM2.5_ATM_ug/m3'], lags=20)
<Figure size 396x396 with 0 Axes>
daily_df=df['PM2.5_ATM_ug/m3'].resample('D')
daily_mean=daily_df.mean()
fig = plt.figure(figsize=(5.5, 5.5))
ax = fig.add_subplot(1,1,1)
df['PM2.5_ATM_ug/m3'].plot(ax=ax, color='b')
daily_mean.plot(ax=ax, color='r')
ax.set_title('By Minutely (blue) & Daily Mean (red)')
ax.set_xlabel('2020 Year record')
ax.set_ylabel('Particulate Matter PM2.5');
from statsmodels.tsa import stattools
adf_result = stattools.adfuller(df['PM2.5_ATM_ug/m3'], autolag='AIC')
print(f'ADF Statistic: {adf_result[0]}')
print(f'n_lags: {adf_result[1]}')
print(f'p-value: {adf_result[1]}')
for key, value in adf_result[4].items():
print('Critial Values:')
print(f' {key}, {value}')
ADF Statistic: -15.79513507741763 n_lags: 1.090499607855049e-28 p-value: 1.090499607855049e-28 Critial Values: 1%, -3.4304667952532175 Critial Values: 5%, -2.8615916212250094 Critial Values: 10%, -2.566797476249605
adf_result
import plotly.express as px
fig=px.line(df,x=df.index,y=['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3'],title='Air Pollution at Port Harcourt, Nigeria')
fig.show()
Spike in the above graph diminished the other values by Particulate Matter Quantity
fig=px.line(df,x=df.index,y=['Temperature_F','Humidity_%'],title='Air Pollution at Port Harcourt, Nigeria')
fig.show()
df.boxplot(column=['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3'],vert=False)
<AxesSubplot:>
df_by_hour=df.resample('1H').mean()
df_by_hour.dropna(inplace=True)
df_by_hour['Day'],df_by_hour['Month']=df_by_hour.index.day_name(),df_by_hour.index.month_name()
fig = px.line(df_by_hour, x=df_by_hour.index, y='PM2.5_ATM_ug/m3', color='Month')
fig.show()
fig = px.line(df_by_hour, x=df_by_hour.index, y='PM2.5_ATM_ug/m3', color='Day')
fig.show()
df_by_day=df.resample('1D').mean()
df_by_day['Day'],df_by_day['Month']=df_by_day.index.day_name(),df_by_day.index.month_name()
fig = px.line(df_by_day, x=df_by_day.index, y='PM2.5_ATM_ug/m3', color='Month')
fig.show()
fig = px.line(df_by_day, x=df_by_day.index, y='PM2.5_ATM_ug/m3', color='Day')
fig.show()
from statsmodels.tsa.seasonal import seasonal_decompose
analysis = df_by_day[['PM2.5_ATM_ug/m3']].copy()
decompose_result_mult = seasonal_decompose(analysis, model="multiplicative")
trend = decompose_result_mult.trend
seasonal = decompose_result_mult.seasonal
residual = decompose_result_mult.resid
decompose_result_mult.plot();
from statsmodels.tsa.seasonal import seasonal_decompose
analysis = df_by_day[['PM2.5_ATM_ug/m3']].copy()
decompose_result_mult = seasonal_decompose(analysis, model="additive")
trend = decompose_result_mult.trend
seasonal = decompose_result_mult.seasonal
residual = decompose_result_mult.resid
decompose_result_mult.plot();
There is large variation in Particulate matter as compared to other quantity
train_size=int(0.8*len(df))
train_df=df.loc[df.index[:train_size]].copy()
test_df=df.loc[df.index[train_size:]].copy()
valid_size=int(0.2*len(test_df))
valid_df=test_df.loc[test_df.index[:valid_size]]
test_df=test_df.loc[test_df.index[valid_size:]]
scaler=MinMaxScaler()
train_df[['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3']]=scaler.fit_transform(train_df[['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3']])
test_df[['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3']]=scaler.transform(test_df[['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3']])
valid_df[['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3']]=scaler.transform(valid_df[['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3']])
train_df.head(3)
| Temperature_F | Humidity_% | PM2.5_ATM_ug/m3 | |
|---|---|---|---|
| created_at | |||
| 2021-07-26 16:45:59+00:00 | 0.5 | 0.35 | 0.070509 |
| 2021-07-26 16:48:03+00:00 | 0.5 | 0.35 | 0.069293 |
| 2021-07-26 16:49:59+00:00 | 0.5 | 0.35 | 0.065486 |
len(train_df)
44841
class TimeSeries(Dataset):
def __init__(self,df,target,features,sequence_length=48,label_length=7):
self.features=features
self.target=target
self.sequence_length=sequence_length
self.label_length=label_length
self.X=df[features].values
self.y=df[target].values
self.X_,self.y_=self.make_sequence()
def make_sequence(self):
X_=[]
y_=[]
L=len(self.X)
for i in range(L-(self.sequence_length+self.label_length)):
X_.append(self.X[i:i+self.sequence_length])
y_.append(self.y[(i+self.sequence_length):(i+self.sequence_length+self.label_length)])
return torch.tensor(X_).float(),torch.tensor(y_).float()
def __len__(self):
return len(self.X_)
def __getitem__(self,idx):
return self.X_[idx],self.y_[idx]
target='PM2.5_ATM_ug/m3'
features=['Temperature_F','Humidity_%','PM2.5_ATM_ug/m3']
train_ds=TimeSeries(train_df,target,features)
test_ds=TimeSeries(test_df,target,features)
valid_ds=TimeSeries(valid_df,target,features)
train_ds[0][0]
tensor([[0.5000, 0.3500, 0.0705],
[0.5000, 0.3500, 0.0693],
[0.5000, 0.3500, 0.0655],
[0.5000, 0.3500, 0.0571],
[0.5000, 0.3500, 0.0653],
[0.5000, 0.3500, 0.0598],
[0.5000, 0.3667, 0.0583],
[0.5000, 0.3500, 0.0624],
[0.5000, 0.3500, 0.0561],
[0.4737, 0.3500, 0.0561],
[0.4737, 0.3500, 0.0571],
[0.4737, 0.3500, 0.0592],
[0.4737, 0.3667, 0.0681],
[0.4737, 0.3667, 0.0586],
[0.4737, 0.3667, 0.0622],
[0.4737, 0.3667, 0.0598],
[0.4737, 0.3667, 0.0592],
[0.4737, 0.3667, 0.0595],
[0.4737, 0.3833, 0.0641],
[0.4737, 0.3667, 0.0636],
[0.4737, 0.3833, 0.0662],
[0.4737, 0.3833, 0.0691],
[0.4737, 0.3833, 0.0729],
[0.4737, 0.3833, 0.0709],
[0.4737, 0.3833, 0.0636],
[0.4737, 0.3833, 0.0641],
[0.4737, 0.3833, 0.0699],
[0.4474, 0.3833, 0.0720],
[0.4474, 0.3833, 0.0748],
[0.4474, 0.4000, 0.0669],
[0.4474, 0.4000, 0.0707],
[0.4474, 0.4000, 0.0738],
[0.4474, 0.4000, 0.0751],
[0.4474, 0.4000, 0.0717],
[0.4474, 0.4000, 0.0773],
[0.4474, 0.4000, 0.0804],
[0.4474, 0.4000, 0.0833],
[0.4474, 0.4000, 0.0853],
[0.4474, 0.4000, 0.0837],
[0.4474, 0.4000, 0.0861],
[0.4474, 0.4000, 0.0905],
[0.4474, 0.4167, 0.0854],
[0.4474, 0.4167, 0.0888],
[0.4474, 0.4167, 0.0874],
[0.4474, 0.4167, 0.0834],
[0.4474, 0.4167, 0.0840],
[0.4474, 0.4167, 0.0903],
[0.4474, 0.4167, 0.0875]])
train_ds[0][0].shape
torch.Size([48, 3])
train_ds[0][1]
tensor([0.0916, 0.0795, 0.0691, 0.0702, 0.0555, 0.0669, 0.0565])
class RegressionLSTM(nn.Module):
def __init__(self,n_feature,n_hidden):
super(RegressionLSTM,self).__init__()
self.n_feature=n_feature
self.n_hidden=n_hidden
self.lstm=nn.LSTM(input_size=self.n_feature,hidden_size=self.n_hidden,batch_first=True,num_layers=1)
self.linear=nn.Linear(in_features=self.n_hidden,out_features=7)
def forward(self,x):
batch_size = x.shape[0]
h0 = torch.zeros(1, batch_size, self.n_hidden).requires_grad_().to(device)
c0 = torch.zeros(1, batch_size, self.n_hidden).requires_grad_().to(device)
_, (hn, _) = self.lstm(x, (h0, c0))
out = self.linear(hn[0])
return out
model=RegressionLSTM(n_feature=3,n_hidden=10).to(device)
loss_function = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
model
RegressionLSTM( (lstm): LSTM(3, 10, batch_first=True) (linear): Linear(in_features=10, out_features=7, bias=True) )
len(train_ds)
44786
train_dl=DataLoader(train_ds,batch_size=64,shuffle=False)
valid_dl=DataLoader(valid_ds,batch_size=64,shuffle=False)
for x,y in train_dl:
print(x.shape)
print(y.shape)
torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) 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torch.Size([64, 7]) torch.Size([64, 48, 3]) torch.Size([64, 7]) torch.Size([50, 48, 3]) torch.Size([50, 7])
x.shape
torch.Size([50, 48, 3])
loss_function(model(x.to(device)),y.to(device))
tensor(0.0557, device='cuda:0', grad_fn=<MseLossBackward>)
train_losses=[]
val_losses=[]
def running_model(model,train_data,valid_data,n_epochs):
best_loss=float('inf')
model=model.to(device)
for epoch in tqdm(range(1,n_epochs+1)):
print(f'{epoch} Epoch is running')
train_loss=0.0
val_loss=[]
for i,(X,y) in enumerate(train_data):
model.train()
X=Variable(X).to(device)
y=Variable(y).to(device)
output = model(X)
loss = loss_function(output, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += round(loss.cpu().item(),5)*X.size(0)
if i%100==0 and i>0:
model.eval()
with torch.no_grad():
losses=0.0
for X, y in valid_data:
X=Variable(X).to(device)
y=Variable(y).to(device)
output = model(X)
losses += round(loss_function(output, y).cpu().item(),5)*X.size(0)
val_loss.append(losses/len(valid_data.dataset))
overall_val_loss=sum(val_loss)/len(val_loss)
if overall_val_loss<best_loss:
best_loss=overall_val_loss
torch.save(model.state_dict(),f'model{epoch}.pth')
train_losses.append(train_loss / len(train_data.dataset))
val_losses.append(overall_val_loss)
running_model(model=model,train_data=train_dl,valid_data=valid_dl,n_epochs=50)
1 Epoch is running 2 Epoch is running 3 Epoch is running 4 Epoch is running 5 Epoch is running 6 Epoch is running 7 Epoch is running 8 Epoch is running 9 Epoch is running 10 Epoch is running 11 Epoch is running 12 Epoch is running 13 Epoch is running 14 Epoch is running 15 Epoch is running 16 Epoch is running 17 Epoch is running 18 Epoch is running 19 Epoch is running 20 Epoch is running 21 Epoch is running 22 Epoch is running 23 Epoch is running 24 Epoch is running 25 Epoch is running 26 Epoch is running 27 Epoch is running 28 Epoch is running 29 Epoch is running 30 Epoch is running 31 Epoch is running 32 Epoch is running 33 Epoch is running 34 Epoch is running 35 Epoch is running 36 Epoch is running 37 Epoch is running 38 Epoch is running 39 Epoch is running 40 Epoch is running 41 Epoch is running 42 Epoch is running 43 Epoch is running 44 Epoch is running 45 Epoch is running 46 Epoch is running 47 Epoch is running 48 Epoch is running 49 Epoch is running 50 Epoch is running
plt.title("Training v/s validation loss at each epoch")
plt.plot(np.arange(1,51),train_losses,label='Train',)
plt.plot(np.arange(1,51),val_losses,label='Validation')
plt.xlabel('Number of epochs')
plt.ylabel('Losses')
plt.legend()
<matplotlib.legend.Legend at 0x198979fb4c0>
test_dl=DataLoader(test_ds,batch_size=1,shuffle=False)
test_loss=[]
model.eval()
with torch.no_grad():
losses=0.0
for X, y in test_dl:
X=Variable(X).to(device)
y=Variable(y).to(device)
output = model(X)
losses += round(loss_function(output, y).cpu().item(),5)
test_loss.append(losses/len(test_dl))
print(f' Overall Test loss :{np.mean(test_loss)}')
Overall Test loss :0.00283036010769576
y.shape
torch.Size([1, 7])
x.shape
torch.Size([50, 48, 3])
df.loc[test_df.index[0]:test_df.index[47]].values.shape
(48, 3)
df.loc[test_df.index[48]:test_df.index[54]].values.shape
(7, 3)
new_df=df.copy()
new_df.loc[:,'PM2.5_Predicted']=0
new_df.head(5)
| Temperature_F | Humidity_% | PM2.5_ATM_ug/m3 | PM2.5_Predicted | |
|---|---|---|---|---|
| created_at | ||||
| 2021-07-26 16:45:59+00:00 | 89 | 50 | 40.78 | 0 |
| 2021-07-26 16:48:03+00:00 | 89 | 50 | 40.09 | 0 |
| 2021-07-26 16:49:59+00:00 | 89 | 50 | 37.93 | 0 |
| 2021-07-26 16:54:03+00:00 | 89 | 50 | 33.18 | 0 |
| 2021-07-26 16:55:59+00:00 | 89 | 50 | 37.84 | 0 |
for i in range(len(df)-55):
X=new_df.loc[new_df.index[i]:new_df.index[i+47],features].values
X=scaler.transform(X)
X=torch.tensor(X).float().unsqueeze(0).to(device)
pred=model(X)
new_df.loc[new_df.index[i+48]:new_df.index[i+47+7],'PM2.5_Predicted']=pred.view(-1).detach().cpu().numpy()
def inverse_PM(x):
return x*(scaler.data_max_[2]-scaler.data_min_[2])+scaler.data_min_[2]
new_df.loc[:,'PM2.5_Predicted']=new_df.loc[:,'PM2.5_Predicted'].apply(inverse_PM)
new_df.tail(8)
| Temperature_F | Humidity_% | PM2.5_ATM_ug/m3 | PM2.5_Predicted | |
|---|---|---|---|---|
| created_at | ||||
| 2021-10-25 09:22:42+00:00 | 87 | 61 | 28.71 | 27.200236 |
| 2021-10-25 09:24:44+00:00 | 87 | 61 | 31.04 | 39.695680 |
| 2021-10-25 09:26:47+00:00 | 87 | 61 | 28.90 | 31.618256 |
| 2021-10-25 09:28:43+00:00 | 87 | 61 | 29.17 | 36.580820 |
| 2021-10-25 09:30:43+00:00 | 87 | 61 | 29.57 | 37.132174 |
| 2021-10-25 09:32:44+00:00 | 87 | 61 | 28.75 | 43.939090 |
| 2021-10-25 09:34:44+00:00 | 87 | 61 | 29.97 | 56.937708 |
| 2021-10-25 09:36:44+00:00 | 87 | 60 | 30.25 | 0.780000 |
fig=px.line(new_df,x=new_df.index,y=['PM2.5_ATM_ug/m3','PM2.5_Predicted'],title='Air Pollution at Port Harcourt, Nigeria')
fig.show()
import gc
gc.collect()
torch.cuda.empty_cache()
Save MinMax scaler for another report
import joblib
joblib.dump(scaler, 'scaler.gz')
#my_scaler = joblib.load('scaler.gz')
['scaler.gz']